class_docstr: General class for handling a Gaussian Process in GPyOpt.
  
  :param kernel: GPy kernel to use in the GP model.
  :param noise_var: value of the noise variance if known.
  :param exact_feval: whether noiseless evaluations are available. IMPORTANT to make the optimization work well in noiseless scenarios (default, False).
  :param optimizer: optimizer of the model. Check GPy for details.
  :param max_iters: maximum number of iterations used to optimize the parameters of the model.
  :param optimize_restarts: number of restarts in the optimization.
  :param sparse: whether to use a sparse GP (default, False). This is useful when many observations are available.
  :param num_inducing: number of inducing points if a sparse GP is used.
  :param verbose: print out the model messages (default, False).
  :param ARD: whether ARD is used in the kernel (default, False).
  :param mean_function: GPy Mapping to use as the mean function for the GP model (default, None).
  
  .. Note:: This model does Maximum likelihood estimation of the hyper-parameters.
docstr: Updates the model with new observations.
docstr_tok: ["Updates","the","model","with","new","observations","."]
code: def updateModel(self, X_all, Y_all, X_new, Y_new):
      """
      Updates the model with new observations.
      """
      if self.model is None:
          self._create_model(X_all, Y_all)
      else:
          self.model.set_XY(X_all, Y_all)
  
      # WARNING: Even if self.max_iters=0, the hyperparameters are bit modified...
      if self.max_iters > 0:
          # --- update the model maximizing the marginal likelihood.
          if self.optimize_restarts == 1:
              self.model.optimize(
                  optimizer=self.optimizer,
                  max_iters=self.max_iters,
                  messages=False,
                  ipython_notebook=False,
              )
          else:
              self.model.optimize_restarts(
                  num_restarts=self.optimize_restarts,
                  optimizer=self.optimizer,
                  max_iters=self.max_iters,
                  verbose=self.verbose,
              )
  
imports: import numpy
  import GPy
  from .base import BOModel
label_desc: Use image node_id 4 for calling the GPModel obj's underlying member method code with example usage: obj.updateModel(X_all, Y_all, X_new, Y_new) without return types